Data Visualizations by County

Rows {data-width = 150}

Confirmed Cases to Date

36,284 (5.8%)

Negative Tests

593,485 (94.2%)

Rows {data-width = 150}

Recovered Cases: 20,062

Active Cases: 15,739

Total Deaths: 483

Column

Cases across time in most populous counties

Cases across time in most populous counties

Logarithm - Cases in populous counties

Logarithm - Cases in populous counties

Row

Cases rate

Cases rate

Case numbers by county

Column

Positive cases by counties with more than 50 cases

All outcomes by counties with more than 50 cases

All outcomes by counties with more than 50 cases

Data Visualizatons by Demographics

Column

Confirmed Cases by Age

Confirmed Cases by Sex

Column

Confirmed Cases by Race

Confirmed Cases by Ethnicity

About

The Tennessee Coronavirus Dashboard

The sole intention of this Coronavirus dashboard is to provide a visual overview of the 2019 Novel COVID-19 as it relates to counties in Tennessee. This dashboard has different graphs for small screens. For more interactive graphs, please view this website on a larger screen (computer/large tablet).

Data

Data is acquired from the New York Times Coronavirus Data, the Tennessee State Data Center, and the Tennessee Department of Health

Last updated: 06-15.

Population data acquired from the US Census.

Created by Malle Carrasco-Harris, PhD using RStudio Flexdashboard and associated packages available in the Source Code.

---
title: "COVID-19 | Tennessee"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    social: menu
    source_code: embed
knit: (function(input_file, encoding) {
 out_dir <- 'docs';
 rmarkdown::render(input_file,
 encoding=encoding,
 output_file=file.path(dirname(input_file), out_dir, 'index.html'))})
---
  

```{r setup, include=FALSE}
library(flexdashboard)
library(readr)
library(ggplot2)
library(plotly)
library(tidyverse)
library(dplyr)

#Acquire Data####
#Load NY Times Github data containing all US Counties###
nyt_path = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv'

counties = read_csv(url(nyt_path))

#Separate Tennessee
tn = counties[ which(counties$state =='Tennessee'),]
tn = tn[which(tn$date < '2020-03-31'),] #The Tennessee data from the new source has data starting March 31

#Tennessee data from TN State Data Center
tn_state = 'https://myutk.maps.arcgis.com/sharing/rest/content/items/32b104abc5d841ca895de7f7c17fc4dc/data'

download.file(tn_state,'TN_COVID19_CountyDaily.xlsx') 

#Data cleaning and processing####
tn_daily =  readxl::read_excel('TN_COVID19_CountyDaily.xlsx',sheet=1) %>%
  filter(DATE > '2020-03-30') %>%
  select(DATE, COUNTY, TEST_POS, TEST_NEG, DEATHS_TOT) %>%
  filter(COUNTY != 'Balance') 

names(tn_daily) = c('Date', 'County', 'Positive', 'Negative', 'Death')

tn_daily$County = ifelse(tn_daily$County == 'Non-Tennessee Resident',
                         "Out of TN",
                         tn_daily$County)

tn_daily$County = ifelse(tn_daily$County == 'Out of State',
                         "Out of TN",
                         tn_daily$County)

tn_daily$County =ifelse(tn_daily$County == 'Dekalb', 
                        'DeKalb', 
                        tn_daily$County)

tn_daily$County =ifelse(tn_daily$County == 'VanBuren', 
                        'Van Buren', 
                        tn_daily$County)

tn_daily$County = as.factor(tn_daily$County)

tn_daily = filter(tn_daily, County !='Probable')

#Merge NYT and Tn Daily dataframes####
tn_daily2 = tn_daily[,c('Date','County', 'Positive', 'Death')]
names(tn_daily2) = c('date','county', 'cases', 'deaths')
tn_daily2 = tn_daily2[!(tn_daily2$county =='Out of TN' | tn_daily2$county =='Pending' | tn_daily2$county == 'Probable'),]
tn_daily2 = tibble::add_column(tn_daily2, state = 'Tennessee', .after='county')

fips_daily =tn %>% group_by(county, fips) %>% tally()

tn_daily2 = left_join(tn_daily2, fips_daily[,1:2], by ='county')
##Row bind tn_daily (TN Health Dept) with tn
tn = rbind(tn, tn_daily2) #Rbind will automatically put the correct columns together. 


#Add population####
#Get Census Population for counties in Tennessee

uscensus = 'https://raw.githubusercontent.com/mfcarrasco/COVID-TN-Counties/master/county_pop_2019.csv'
tn_pop = read_csv(url(uscensus))
tn_pop = tn_pop[ which(tn_pop$State =='Tennessee'),]
tn_pop = tn_pop[-1,c(2:3)]
tn_pop$County = gsub(' County', '', tn_pop$County)
tn_pop$Population = as.numeric(tn_pop$Population)
tn_pop = tn_pop[, c('County', 'Population')]
names(tn_pop) = c('county', 'population')

##Combine tn (NYT) dataframe with Population
tn = left_join(tn, tn_pop, by='county')
tn$county = as.factor(tn$county)

#Calculate per 10,000 residents
tn['cases_per_tenk'] = (tn$cases/tn$population)*10^4
#Tn dataframe is ready for long term data visualiations and includes standardization by population.


#Keep most recent for tn_daily
tn_daily = tn_daily %>% group_by(County) %>% top_n(1, Date)

#Clean the global environment###
rm(list=ls()[!ls() %in% c('tn', 'tn_daily')])



#Value Box Calculations####
tn_ext =  readxl::read_excel('TN_COVID19_COUNTYDaily.xlsx',sheet=2) %>%
  top_n(1,DATE) %>%
  filter(COUNTY != 'Balance') 

tn_ext$COUNTY = ifelse(tn_ext$COUNTY == 'Non-Tennessee Resident',
                         "Out of TN",
                         tn_ext$COUNTY)


tn_ext$COUNTY = as.factor(tn_ext$COUNTY)

#Total Cases

total_cases = sum(tn_ext$TEST_POS)
total_negative = sum(tn_ext$TEST_NEG)
total_death = sum(tn_ext$DEATHS_TOT)

total_recov = sum(tn_ext$RECOV_TOT)
active_cases = total_cases - total_death - total_recov #sum(tn_ext$ACTIVE_TOT)

total_tests = total_cases + total_negative

ks = function(x) {scales::number_format(accuracy = 1, scale = 1/1000, suffix = 'K')(x)}

```

Data Visualizations by County
=======================================

Rows {data-width = 150}
-----------
### Confirmed Cases to Date

```{r}
#Total Positive Cases
cases_per = ((total_cases/total_tests)*100) %>% 
  round(1) %>% 
  paste0('%')

total_cases_vb = total_cases %>% 
  formattable::comma(digits=0) %>% 
  paste0(' (',cases_per,')') 

valueBox(value = total_cases_vb, icon='fa-user-plus', color='#002D65')
```

### Negative Tests 

```{r} 
#Total Negative Cases
negative_per = ((total_negative/total_tests)*100) %>% 
  round(1) %>% paste0('%')

total_negative_vb = total_negative %>% 
  formattable::comma(digits=0) %>% paste0(' (', negative_per,')') 

valueBox(value = total_negative_vb, icon='fa-user-minus', color='#CC0000')
```


Rows {data-width = 150}
-----------

### Recovered Cases: `r total_recov %>% formattable::comma(digits=0)`
```{r}
recov_per = ((total_recov/(total_cases))*100) %>% round(1)

gauge(recov_per, min=0, max = 100, symbol = '%')
```

### Active Cases: `r active_cases %>% formattable::comma(digits=0)`
```{r}
active_per = ((active_cases/(total_cases))*100) %>% round(1) 

gauge(active_per, min=0, max = 100, symbol = '%', 
      gaugeSectors(
        success = c(0,25), warning = c(26,100)))
```

### Total Deaths: `r total_death %>% formattable::comma(digits=0)` 

```{r} 
 #Total Deaths Cases
death_per = ((total_death/total_cases)*100) %>% round(1) %>% paste0('%')

gauge(death_per, min=0, max = 100, symbol = '%', 
      gaugeSectors(
        success = c(0,5), warning = c(6,100)))
```


Column {}
-----------------------------------------------------------------------

### Cases across time in most populous counties

```{r}

tn_top =c('Shelby', 'Davidson', 'Knox', 'Hamilton', 'Rutherford', 'Williamson')
tn_top = tn[ tn$county %in% tn_top,]


t_line = tn_pop_line =ggplot(data=tn_top, aes(x=date, y=cases, color=county))+
  geom_line(size=1)+
  scale_x_date(expand = c(0,0), date_breaks = '1 week', date_labels = '%b %d')+
  scale_y_continuous(labels = ks)+
  labs(x='', y='Cases')+
  theme(legend.position = 'none', 
        panel.background = element_blank(), 
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.text=element_text(face='bold'),
        axis.text.x = element_text(angle=45, hjust=1))+
  scale_color_brewer(palette = 'Spectral',direction=-1)
ggplotly(t_line)
```

### Cases across time in most populous counties {.mobile}

```{r}
tn_top =c('Shelby', 'Davidson', 'Knox', 'Hamilton', 'Rutherford', 'Williamson')
tn_top = tn[ tn$county %in% tn_top,]

ggplot(data=tn_top, aes(x=date, y=cases, color=county))+
  geom_line(size=1)+
  scale_x_date(expand = c(0,0), date_breaks = '1 week', date_labels = '%m-%d')+
  scale_y_continuous(labels = ks)+
  labs(x='', y='Cases')+
  theme(legend.title = element_blank(), 
        panel.background = element_blank(), 
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.text=element_text(face='bold'),
        axis.text.x = element_text(angle=45, hjust=1),
        legend.position ='top',
        legend.box = 'horizontal')+
  scale_color_brewer(palette = 'Spectral',direction=-1)
```

### Logarithm - Cases in populous counties
```{r}
tn_top =c('Shelby', 'Davidson', 'Knox', 'Hamilton', 'Rutherford', 'Williamson')
tn_top = tn[ tn$county %in% tn_top,]


logplot = ggplot(data=tn_top, aes(x=date, y=cases, color=county))+
  geom_line(size=1)+
  scale_x_date(expand = c(0,0), date_breaks = '1 week', date_labels = '%b %d')+
  labs(x='', y='Cases')+
  theme(legend.title = element_blank(), 
        panel.background = element_blank(), 
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.text=element_text(face='bold'),
        axis.text.x = element_text(angle=45, hjust=1))+
  scale_color_brewer(palette = 'Spectral',direction=-1)+
  scale_y_log10(breaks=scales::trans_breaks('log10', function(x) 10^x))
ggplotly(logplot)
```

### Logarithm - Cases in populous counties {.mobile}
```{r}
tn_top =c('Shelby', 'Davidson', 'Knox', 'Hamilton', 'Rutherford', 'Williamson')
tn_top = tn[ tn$county %in% tn_top,]


ggplot(data=tn_top, aes(x=date, y=cases, color=county))+
  geom_line(size=1)+
  scale_x_date(expand = c(0,0), date_breaks = '1 week', date_labels = '%b %d')+
  labs(x='', y='Cases')+
  theme(legend.title = element_blank(), 
        panel.background = element_blank(), 
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.text=element_text(face='bold'),
        axis.text.x = element_text(angle=45, hjust=1),
        legend.position ='top',
        legend.box = 'horizontal')+
  scale_color_brewer(palette = 'Spectral',direction=-1)+
  scale_y_log10(breaks=scales::trans_breaks('log10', function(x) 10^x))

```

Row {data-width=400}
-------------------------
### Cases rate 
```{r}
library(usmap)
library(viridis)
tn_geo =tn %>% group_by(county) %>% top_n(1,date)
tn_geo = tn_geo[!(tn_geo$county =='Unknown'),]
tn_geo$fips =fips(state = 'TN', county=tn_geo$county)

library(rjson)
url = 'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
json_file <- rjson::fromJSON(file=url)

#Create map

fig <- plot_ly() %>% 
  add_trace(
    text = paste(tn_geo$county,' County'),
    hoverinfo = 'text',
    type='choroplethmapbox',
    geojson= json_file,
    locations=tn_geo$fips,
    z = tn_geo$cases_per_tenk,
    zmin=0,
    zmax = round(max(tn_geo$cases_per_tenk),-3),
    colorscale='Viridis',
    marker=list(line=list(
      width=0),
      opacity=0.9)) %>% 
  layout(mapbox=list(
    style="carto-positron",
    zoom =5.05,
    center=list(lon= -86.7816, lat=36.1627))) %>%
  colorbar(title = "Cases per 10,000") 
fig
```

### Cases rate {.mobile}
```{r}
#Create mobile-friendly map

plot_usmap(include = 'TN',
           regions = 'counties',
           data = tn_geo,
           values = 'cases_per_tenk') +
  scale_fill_viridis(name = 'Cases per 10K')+
  theme(legend.position = 'top')
```

### Case numbers by county {.no-mobile}
```{r}
tn_daily[,2:5] %>%
  DT::datatable(rownames = FALSE,
                colnames = c('County', 'Confirmed', 'Negative', 'Death'),
                options = list(pageLength = 10))
```

Column {data-width=350, data-height=470}
-----------------------------------------------------------------------

### Positive cases by counties with more than 50 cases {.no-mobile}

```{r}
tn_cases = tn_daily[which(tn_daily$Positive >50 & 
                            tn_daily$County != 'Pending'  &
                            tn_daily$County != 'Out of TN'), 
                    c('County', 'Positive','Negative','Death')] 
plot_ly(data=tn_cases,
        x=tn_cases$Positive,
        y=reorder(tn_cases$County, tn_cases$Positive),
        type='bar',
        orientation='h', 
        marker= list(color='#002D65')) %>%
  layout(xaxis = list(title= 'Count', 
                      zeroline = FALSE, 
                      showline = F, 
                      showticklabels = T, 
                      showgrid = F),
         yaxis = list(showgrid = FALSE, 
                      showline = FALSE, 
                      showticklabels = TRUE,
                      dtick=1,
                      tickfont = list(size=10)))
```



### All outcomes by counties with more than 50 cases

```{r}
tn_cases = tn_daily[which(tn_daily$Positive > 50 & 
                            tn_daily$County != 'Pending'  &
                            tn_daily$County != 'Out of TN'), c('County', 'Positive','Negative','Death')] #Remove where there are no cases

plot_ly(data=tn_cases,
        x= reorder(tn_cases$County, tn_cases$Negative),
        y=tn_cases$Negative,
        type='bar',
        name='Negative Cases',
        marker= list(color='grey')) %>%
          add_trace(y = tn_cases$Positive,
                    name='Positive Cases',
                    marker = list(color='#002D65')) %>%
          add_trace(y = tn_cases$Death,
                    name='Deaths',
                    marker = list(color='#CC0000')) %>%
          layout(barmode = 'stack',
                 xaxis = list(showgrid = FALSE, 
                              showlilnee = FALSE, 
                              showticklabels = TRUE,
                              dtick=1,
                              tickfont =list(size=10)),
                 yaxis = list(title= 'Count', 
                              zeroline = FALSE, 
                              showline = F, 
                              showticklabels = T, 
                              showgrid = F),
                 hovermode = 'compare')
```

### All outcomes by counties with more than 50 cases {.mobile}

```{r}
tn_cases = tn_daily[which(tn_daily$Positive > 50 & 
                            tn_daily$County != 'Pending'  &
                            tn_daily$County != 'Out of TN'), 
                    c('County', 'Death','Negative','Positive')] %>%
  gather(Cases, Count, Death:Positive) %>% 
  mutate(Cases = factor(Cases, levels = c("Death", "Positive", "Negative")))

ggplot(tn_cases,aes(y=reorder(County, Count, sum), x= Count, fill = Cases))+
  geom_bar(position='stack', stat =  'identity')+
  labs(x='Count', y='')+
  theme(legend.title = element_blank(), 
        panel.background = element_blank(), 
        axis.line = element_blank(),
        axis.ticks = element_blank(),
              axis.text = element_text(face = 'bold'),
              legend.direction='horizontal',
              legend.position = 'top')+
  scale_fill_manual(values = c(Death = '#CC0000', Positive = '#002D65', 'Negative' = 'grey')) +
  scale_x_continuous(labels = ks,breaks = seq(min(tn_cases$Count), max(tn_cases$Count)*1.5, by=20000))
```


















































































































































































Data Visualizatons by Demographics
=====================================

Column {data-width=350, data-height=450}
---------------------------
### Confirmed Cases by Age
```{r}
#Get US Census Demographic Data 
census_demo = 'https://raw.githubusercontent.com/mfcarrasco/COVID-TN-Counties/master/census_demographics.xlsx'

age_census = readxl::read_excel('census_demographics.xlsx',sheet='Age') %>% 
  select(Age, Percent)%>%
  rename('Census_Percent' = 'Percent') 

#Get TN Cases Data
tn_age = 'https://myutk.maps.arcgis.com/sharing/rest/content/items/1bdfe86c38514c9c878241d5230d9a85/data'

download.file(tn_age,'TN_Age.xlsx') 

tn_age =  readxl::read_excel('TN_Age.xlsx',sheet=1) %>% 
  top_n(1,DATE) %>%
  select(DATE, AGE, TOT_CASE_COUNT, DEATHS_TOT)

names(tn_age) = c('Date', 'Age', 'Count',  'Deaths')

tn_age$Age = as.factor(tn_age$Age)
tn_age$Case_Percent = (tn_age$Count/sum(tn_age$Count))*100
tn_age$Death_Percent =(tn_age$Deaths/sum(tn_age$Deaths))*100

tn_age = cbind(tn_age[,c('Age', 'Case_Percent','Death_Percent')], age_census[,2])
tn_age$Census_Percent[10] = NA

fills = c('Case_Percent' = '#002D65', 'Death_Percent' = '#CC0000', 'Census_Percent' = 'grey')

ggplot(tn_age,aes(x=Age))+
  geom_col(aes(y = Census_Percent, fill='Census_Percent'),width = .75)+
  geom_col(aes(y = Case_Percent, fill= 'Case_Percent'),width = .5)+
  geom_col(aes(y = Death_Percent, fill='Death_Percent'),width = .1)+
  theme(panel.background = element_blank(), 
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_blank(), 
        axis.text = element_text(face = 'bold'),
        axis.text.x = element_text(angle=30),
        legend.title = element_blank(),
        legend.position = c(.2,.90),
        legend.box.just = 'left')+
  scale_fill_manual(breaks=c('Census_Percent', 'Case_Percent', 'Death_Percent'),
                    values=fills,
                    labels=c('Population %', 'Cases %', 'Deaths %'))


```

### Confirmed Cases by Sex
```{r}
#Get US Census Demographic Data - Sex
sex_census = readxl::read_excel('census_demographics.xlsx',sheet='Sex') %>% 
  rename('Census_Percent' = 'Percent')

#Get TN Cases Data
tndh_demo = 'https://www.tn.gov/content/dam/tn/health/documents/cedep/novel-coronavirus/datasets/Public-Dataset-RaceEthSex.XLSX'

download.file(tndh_demo,'TNDeptHealth_Demographics.xlsx') 

tndh_demo =  readxl::read_excel('TNDeptHealth_Demographics.xlsx',sheet=1) %>% 
  top_n(1,Date) %>%
  group_split(Category)

tn_sex = tndh_demo[[3]] %>% 
  select(-c('Date', 'Category'))
  
names(tn_sex) = c('Sex', 'Count', 'Case_Percent', 'Deaths', 'Death_Percent')

tn_sex = tn_sex %>% 
  mutate(Case_Percent = Case_Percent*100) %>%
  mutate(Death_Percent = Death_Percent*100) %>%
  select(Sex, Case_Percent, Death_Percent) %>%
  left_join(sex_census, 'Sex')

fills = c('Case_Percent' = '#002D65', 'Death_Percent' = '#CC0000', 'Census_Percent' = 'grey')

ggplot(tn_sex,aes(x=Sex))+
  geom_col(aes(y = Census_Percent, fill='Census_Percent'),width = .75)+
  geom_col(aes(y = Case_Percent, fill= 'Case_Percent'),width = .5)+
  geom_col(aes(y = Death_Percent, fill='Death_Percent'),width = .1)+
  theme(panel.background = element_blank(), 
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_blank(), 
        axis.text = element_text(face = 'bold'),
        axis.text.x = element_text(angle=30),
        legend.title = element_blank(),
        legend.position = c(.85,.90),
        legend.box.just = 'left')+
  scale_fill_manual(breaks=c('Census_Percent', 'Case_Percent', 'Death_Percent'),values=fills,labels=c('Population %', 'Cases %', 'Deaths %'))
```


Column {data-width=350, data-height=450}
---------------------------

### Confirmed Cases by Race
```{r}
#Get US Census Demographic Data - Race
race_census = readxl::read_excel('census_demographics.xlsx',sheet='Race') %>% 
  select(Race = Race, Census_Percent =Percent)

tn_race = tndh_demo[[2]] %>% 
  select(-c('Date', 'Category'))
  
names(tn_race) = c('Race', 'Count', 'Case_Percent', 'Deaths', 'Death_Percent')

tn_race = tn_race %>% 
  mutate(Case_Percent = Case_Percent*100) %>%
  mutate(Death_Percent = Death_Percent*100) %>%
  select(Race, Case_Percent, Death_Percent) %>%
  left_join(race_census, 'Race') %>%
  mutate(Race = factor(Race, levels = c('Asian', 'Black or African American', 'White', 'Other/Multiracial', 'Pending')))


fills = c('Case_Percent' = '#002D65', 'Death_Percent' = '#CC0000', 'Census_Percent' = 'grey')

ggplot(tn_race,aes(y=Race))+
  geom_col(aes(x = Census_Percent, fill='Census_Percent'),width = .75)+
  geom_col(aes(x = Case_Percent, fill= 'Case_Percent'),width = .5)+
  geom_col(aes(x = Death_Percent, fill='Death_Percent'),width = .1)+
  scale_y_discrete(limits = rev(levels(tn_race$Race)))+
  theme(panel.background = element_blank(), 
        axis.line = element_blank(), 
        axis.title=element_blank(),
        axis.ticks = element_blank(),
        axis.text = element_text(face = 'bold'),
        axis.text.x = element_text(angle=30, h=1),
        legend.title = element_blank(), 
        legend.position = c(.85,.90),
        legend.box.just = 'left')+
  scale_fill_manual(breaks=c('Census_Percent', 'Case_Percent', 'Death_Percent'),values=fills,labels=c('Population %', 'Cases %', 'Deaths %'))
```

### Confirmed Cases by Ethnicity
```{r}
#Get US Census Demographic Data - Ethnicity
eth_census = readxl::read_excel('census_demographics.xlsx',sheet='Ethnicity') %>% 
  select(Ethnicity=Ethnicity,Census_Percent =Percent)

tn_eth = tndh_demo[[1]] %>% 
  select(-c('Date', 'Category'))
  
names(tn_eth) = c('Ethnicity', 'Count', 'Case_Percent', 'Deaths', 'Death_Percent')

tn_eth = tn_eth %>% 
  mutate(Ethnicity = ifelse(Ethnicity == 'Hispanic', 'Hispanic or Latino', Ethnicity)) %>% 
  mutate(Case_Percent = Case_Percent*100) %>%
  mutate(Death_Percent = Death_Percent*100) %>%
  select(Ethnicity, Case_Percent, Death_Percent) %>%
  left_join(eth_census, 'Ethnicity') %>% 
  mutate(Ethnicity = factor(Ethnicity, levels = c('Hispanic or Latino','Not Hispanic or Latino', 'Pending')))


fills = c('Case_Percent' = '#002D65', 'Death_Percent' = '#CC0000', 'Census_Percent' = 'grey')

ggplot(tn_eth,aes(y=Ethnicity))+
  geom_col(aes(x = Census_Percent, fill='Census_Percent'),width = .75)+
  geom_col(aes(x = Case_Percent, fill= 'Case_Percent'),width = .5)+
  geom_col(aes(x = Death_Percent, fill='Death_Percent'),width = .1)+
  scale_y_discrete(limits = rev(levels(tn_eth$Ethnicity)), position='right')+
  scale_x_reverse()+
  theme(panel.background = element_blank(), 
        axis.line = element_blank(), 
        axis.title=element_blank(),
        axis.ticks = element_blank(),
        axis.text = element_text(face = 'bold'),
        axis.text.x = element_text(angle=30, h=1),
        legend.title = element_blank(), 
        legend.position = c(.15,.90),
        legend.box.just = 'left')+
  scale_fill_manual(breaks=c('Census_Percent', 'Case_Percent', 'Death_Percent'),values=fills,labels=c('Population %', 'Cases %', 'Deaths %'))
```

About 
================================

**The Tennessee Coronavirus Dashboard**    
  
The sole intention of this Coronavirus dashboard is to provide a visual overview of the 2019 Novel COVID-19 as it relates to counties in Tennessee. This dashboard has different graphs for small screens. For more interactive graphs, please view this website on a larger screen (computer/large tablet).   


**Data**

Data is acquired from the [New York Times Coronavirus Data](https://github.com/nytimes/covid-19-data), the [Tennessee State Data Center](https://myutk.maps.arcgis.com/home/group.html?id=c98fc99308dd43fb98146d3cf21fc31c&q=tags%3A%22COVID-19%22&view=list&focus=files#content), and the [Tennessee Department of Health](https://www.tn.gov/health/cedep/ncov.html)

Last updated: `r max(tn$date) %>% format('%m-%d')`.

Population data acquired from the [US Census](https://data.census.gov/cedsci/table?q=Tennessee%20race%20demographics&g=0400000US47&tid=ACSDP1Y2018.DP05&hidePreview=true).



Created by [Malle Carrasco-Harris, PhD](https://www.linkedin.com/in/malle-carrasco-harris) using [RStudio Flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) and associated packages available in the Source Code.